AChemS

library(tidyverse)

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NGS <- read_csv("NGS.csv")

## Warning: Missing column names filled in: 'X1' [1]

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## cols(
##   .default = col_double()
## )

## See spec(...) for full column specifications.

NGS_Small <-NGS

df <- NGS_Small %>%
  filter(SEX != "0" & totcorr !=7 & SMOKE != "0") %>%
  filter(ETHNIC !="0" & ETHNIC != "7") %>% 
  filter(decage !="0") %>%
  #Now we are averaging by sex and age
  group_by(SEX,decage) %>% 
  summarize(totcorr = mean(totcorr))

## `summarise()` regrouping output by 'SEX' (override with `.groups` argument)

ggplot(df, aes(x = decage, y = totcorr, color=as.factor(SEX)))+
  geom_point()

ggplot(df, aes(x = decage, y = totcorr, color=as.factor(SEX)))+
  geom_point()+
  labs(y="Number Correct")+
  scale_color_manual(name="Sex",labels=c("Male","Female"),values=c("blue","red"))+
  scale_x_continuous(name="Age in Decades",breaks=c(1:9),labels=c("Teens","20's","30's","40's","50's","60's","70's","80's","90's"))

library(plotly)

## 
## Attaching package: 'plotly'

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##     layout

AS<-ggplot(df, aes(x = decage, y = totcorr, color=as.factor(SEX)))+
  geom_point(aes(frame = decage))+
  labs(y="Number Correct")+
  scale_color_manual(name="Sex",labels=c("Male","Female"),values=c("blue","pink"))+
  scale_x_continuous(name="Age in Decades",breaks=c(1:9),labels=c("Teens","20's","30's","40's","50's","60's","70's","80's","90's"))+
   theme_classic()

## Warning: Ignoring unknown aesthetics: frame

ggplotly(AS)